FOSTERING DEMENTIA INCLUSIVE NEIGHBORHOODS: A GIS ANALYSIS OF NEIGHBORHOOD WALKABILITY FOR PERSONS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Active engagement in the natural environment is an important aspect of health and wellbeing for persons with dementia. Neighborhoods with high walkability are critical for persons with dementia to access the outdoors and promote ageing in place, while allowing for persons with dementia to maintain social relationships, connect with nature, and foster a sense of freedom. Although persons living with dementia are affected by general walkability factors such as presence of sidewalks and the availability of destinations such as health services or recreational facilities, dementia-inclusive neighborhoods must also be distinctive and easy to navigate, as persons with dementia are more prone to become disoriented or lost. Therefore, the Dementia-Inclusive Streets and Community Access, Participation and Engagement (DemSCAPE) project sought to identify how persons living with dementia interact with their environment and how municipalities can implement change to make cities more walkable for PLWD. A GIS analysis was completed to assess current infrastructural barriers and facilitators for aging-in-place for persons living with dementia. This analysis, which included interviews with individuals living with dementia and their family caregivers, found that their neighborhoods were severely lacking the infrastructure needed to support walkability for persons living with dementia. From the absence of sidewalks to disorienting neighborhood patterns, the city of focus has a long way to go in supporting aging-in-place for persons living with dementia. This presentation will showcase how these findings can be used to influence protocols in municipalities to implement supportive infrastructure for persons living with dementia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".